class_docstr: Balanced L1 Loss.
  
  arXiv: https://arxiv.org/pdf/1904.02701.pdf (CVPR 2019)
  
  Args:
      alpha (float): The denominator ``alpha`` in the balanced L1 loss.
          Defaults to 0.5.
      gamma (float): The ``gamma`` in the balanced L1 loss. Defaults to 1.5.
      beta (float, optional): The loss is a piecewise function of prediction
          and target. ``beta`` serves as a threshold for the difference
          between the prediction and target. Defaults to 1.0.
      reduction (str, optional): The method that reduces the loss to a
          scalar. Options are "none", "mean" and "sum".
      loss_weight (float, optional): The weight of the loss. Defaults to 1.0
code: def __init__(
      self,
      alpha=0.5,
      gamma=1.5,
      beta=1.0,
      reduction="mean",
      loss_weight=1.0,
  ):
      super(BalancedL1Loss, self).__init__()
      self.alpha = alpha
      self.gamma = gamma
      self.beta = beta
      self.reduction = reduction
      self.loss_weight = loss_weight
  
file_path: mmdetection/mmdet/models/losses/balanced_l1_loss.py
imports: import numpy
  import torch
  import torch.nn
  from mmdet.registry import MODELS
  from .utils import weighted_loss
label_desc: Use image node_id 1 to create a new BalancedL1Loss object from inherited base classes: nn with example: obj = BalancedL1Loss(alpha, gamma, beta, reduction, loss_weight)